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overparameterized machine learning

topic1 events
papersSEP 10 04:00 UTC

Survey paper reviews overparameterized machine learning and the bias-variance tradeoff

A new overview article on arXiv surveys the theory of overparameterized machine learning, in which models with far more parameters than training examples still achieve strong performance. The paper explains how such behavior conflicts with the classical bias-variance tradeoff and organizes recent theoretical work developed to explain it. It serves as a structured introduction for readers interested in the statistical foundations of modern deep learning.

deep-learning/theory/generalization/survey